Artificial Synapse Based on a 2D-SnO2 Memtransistor with Dynamically Tunable Analog Switching for Neuromorphic Computing
Source abstract
Abstract
A new type of two-dimensional (2D) SnO2 semiconductor-based gate-tunable memristor, that is, a memtransistor, an integrated device of a memristor and a transistor, was demonstrated to advance next-generation neuromorphic computing technology. The polycrystalline 2D-SnO2 memristors derived from a low-temperature and vacuum-free liquid metal process offer several interesting resistive switching properties such as excellent digital/analog resistive switching, multistate storage, and gate-tunability function of resistance switching states. Significantly, the gate tunability function that is not achievable in conventional two-terminal memristors provides the capability to implement heterosynaptic analog switching by regulating gate bias for enabling complex neuromorphic learning. We successfully demonstrated that the gate-tunable synaptic device dynamically modulated the analog switching behavior with good linearity and an improved conductance change ratio for high recognition accuracy learning. The presented gate-tunable 2D-oxide memtransistor will advance neuromorphic device technology and open up new opportunities to design learning schemes with an extra degree of freedom.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Memory and Neural Computing · Transition Metal Oxide Nanomaterials · Ferroelectric and Negative Capacitance Devices
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Dr., La Jolla, California 92093, United States
Department of Materials Science and Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
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